Privacy-Preserving Data Mining: Models and Algorithms
By Charu C. Aggarwal, Philip S. Yu. 2008
652 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/16c9a99680bda420806a6df5e34ede696868db94
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Indrė Žliobaitė
- Fosca Giannotti
- Fei Wang
- Charu C. Aggarwal
- Toshihiro Kamishima
- Benjamin C. M. Fung
- Dino Pedreschi
- Anna Monreale
Citations in the corpus
- Discrimination- and privacy-aware patterns (2015, methodology): “A detailed description of different PPDM models and methods can be found in Aggarwal and Yu (2008); Fung et al. (2010). One key challenge in PPDM originates from the following privacy question (Kantarcioglu et al. 2004): do the data mining results themselves violate privacy? In other words, may the ”
- Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “We refer readers interested in PPDDM to work by [Clifton et al. 2002; Kantarcioglu 2008; Pinkas 2002; Vaidya 2008; Wright et al. 2005].”
- Efficiency Improvement of Neutrality-Enhanced Recommendation (2013, methodology): “Mutual information is normalized into the range [0, 1] by employing the geometrical mean as described in [6].”
- Recommender Systems (2016, unknown): “A general discussion of privacy-preservation methods may be found in [20].”
- Mining Our Reality (2009, background): “Meanwhile, new algorithms aim to address privacy concerns arising from data sharing and aggregation ( 9 , 10 ).”
- Fairness-Aware Classifier with Prejudice Remover Regularizer (2012, background): “The removal of prejudice is closely related to privacy-preserving data mining [1], which is a technology for mining useful information without exposing individual private records.”
- Privacy Preserving Decision Tree Learning Using Unrealized Data Sets (2012, background): “DATA mining is widely used by researchers for scienceand business purposes.”
- Supervised patient similarity measure of heterogeneous patient records (2012, background): “Comdi is related to the area of privacy preserving data mining [1].”